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

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[TOC]

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# Introduction
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## Purpose
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`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.
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## Principles
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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

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


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# Basic usage

## Starting with a fresh database
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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 !


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## Creating a series
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However here's a simple example:
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```python
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 >>> from sqlalchemy import create_engine
 >>> import pandas as pd
 >>> from tshistory.tsio import TimeSerie
 >>>
 >>> engine = create_engine('postgres://me:password@localhost/mydb')
 >>> tsh = TimeSerie()
 >>>
 >>> series = pd.Series([1, 2, 3],
 ...                    pd.date_range(start=pd.Timestamp(2017, 1, 1),
 ...                                  freq='D', periods=3))
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 # db insertion
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 >>> tsh.insert(engine, 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
 >>> tsh.get(engine, 'my_series')
 2017-01-01    1.0
 2017-01-02    2.0
 2017-01-03    3.0
 Name: my_series, dtype: float64
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```

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## Updating a series
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This is good. Now, let's insert more:
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```python
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 >>> series = pd.Series([2, 7, 8, 9],
 ...                    pd.date_range(start=pd.Timestamp(2017, 1, 2),
 ...                                  freq='D', periods=4))
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 # db insertion
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 >>> tsh.insert(engine, 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)
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 # db retrieval
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 >>> 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
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```

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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.

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## Retrieving history

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

```python
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 >>> history = tsh.get_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
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```

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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:
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```python
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 >>>diffs = tsh.get_history(engine, 'my_series', diffmode=True)
 >>> for idate, series in tsh.get_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
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```
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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]', 'index_names': [], 'value_dtype': '<f8'}
```

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

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## Basic operations

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

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

 Options:
   --help  Show this message and exit.

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Commands:
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  check    coherence checks of the db
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  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
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```

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