# Indexer timeseries

**URL:** https://forum.duniter.org/t/indexer-timeseries/11281
**Category:** Indexers
**Created:** [20 June 2023 11:06 UTC](https://forum.duniter.org/t/indexer-timeseries/11281 "2023-06-20T11:06:36Z")
**Posts on this page:** 1
**Page:** 1

<div class="post-metadata">

### Author: ![HugoTrentesaux](https://forum.duniter.org/user_avatar/forum.duniter.org/hugotrentesaux/32/6396_2.png) [@HugoTrentesaux](https://forum.duniter.org/u/HugoTrentesaux)
#### Post date: [20 June 2023 11:06 UTC](https://forum.duniter.org/t/indexer-timeseries/11281/1 "2023-06-20T11:06:37Z")

</div>

@ManUtopiK worked on a feature of the indexer allowing to do simple requests to get transactions timeseries. This weekend we put this in production on my [https://indexer.gdev.coinduf.eu/](https://indexer.gdev.coinduf.eu/) and we did small proof of concepts.

On the playground, use this example request:

```auto
query timeserie {
  transaction_timeserie(args: {period: "month", from: "2017-03-01", to: "2024-01-01"}) {
      amount
      date
    }
}

```

> **Result**
>
> ```json
> {
> "data": {
> "transaction_timeserie": [
> {
> "amount": 273466,
> "date": "2017-03-01T00:00:00+00:00"
> },
> {
> "amount": 545861,
> "date": "2017-04-01T00:00:00+00:00"
> },
> {
> "amount": 1358885,
> "date": "2017-05-01T00:00:00+00:00"
> },
> {
> "amount": 2491672,
> "date": "2017-06-01T00:00:00+00:00"
> },
> {
> "amount": 848601,
> "date": "2017-07-01T00:00:00+00:00"
> },
> {
> "amount": 2234925,
> "date": "2017-08-01T00:00:00+00:00"
> },
> {
> "amount": 3027654,
> "date": "2017-09-01T00:00:00+00:00"
> },
> {
> "amount": 8132790,
> "date": "2017-10-01T00:00:00+00:00"
> },
> {
> "amount": 17240912,
> "date": "2017-11-01T00:00:00+00:00"
> },
> {
> "amount": 6621283,
> "date": "2017-12-01T00:00:00+00:00"
> },
> {
> "amount": 10011582,
> "date": "2018-01-01T00:00:00+00:00"
> },
> {
> "amount": 6610732,
> "date": "2018-02-01T00:00:00+00:00"
> },
> {
> "amount": 23617532,
> "date": "2018-03-01T00:00:00+00:00"
> },
> {
> "amount": 9799309,
> "date": "2018-04-01T00:00:00+00:00"
> },
> {
> "amount": 19785218,
> "date": "2018-05-01T00:00:00+00:00"
> },
> {
> "amount": 14570588,
> "date": "2018-06-01T00:00:00+00:00"
> },
> {
> "amount": 9618677,
> "date": "2018-07-01T00:00:00+00:00"
> },
> {
> "amount": 10280283,
> "date": "2018-08-01T00:00:00+00:00"
> },
> {
> "amount": 17780740,
> "date": "2018-09-01T00:00:00+00:00"
> },
> {
> "amount": 15231073,
> "date": "2018-10-01T00:00:00+00:00"
> },
> {
> "amount": 28678706,
> "date": "2018-11-01T00:00:00+00:00"
> },
> {
> "amount": 39534324,
> "date": "2018-12-01T00:00:00+00:00"
> },
> {
> "amount": 25654906,
> "date": "2019-01-01T00:00:00+00:00"
> },
> {
> "amount": 29262504,
> "date": "2019-02-01T00:00:00+00:00"
> },
> {
> "amount": 50969516,
> "date": "2019-03-01T00:00:00+00:00"
> },
> {
> "amount": 24133930,
> "date": "2019-04-01T00:00:00+00:00"
> },
> {
> "amount": 31986976,
> "date": "2019-05-01T00:00:00+00:00"
> },
> {
> "amount": 25593902,
> "date": "2019-06-01T00:00:00+00:00"
> },
> {
> "amount": 23591320,
> "date": "2019-07-01T00:00:00+00:00"
> },
> {
> "amount": 31578056,
> "date": "2019-08-01T00:00:00+00:00"
> },
> {
> "amount": 46333776,
> "date": "2019-09-01T00:00:00+00:00"
> },
> {
> "amount": 46112168,
> "date": "2019-10-01T00:00:00+00:00"
> },
> {
> "amount": 47455328,
> "date": "2019-11-01T00:00:00+00:00"
> },
> {
> "amount": 85977304,
> "date": "2019-12-01T00:00:00+00:00"
> },
> {
> "amount": 57056968,
> "date": "2020-01-01T00:00:00+00:00"
> },
> {
> "amount": 37898168,
> "date": "2020-02-01T00:00:00+00:00"
> },
> {
> "amount": 37908144,
> "date": "2020-03-01T00:00:00+00:00"
> },
> {
> "amount": 23775406,
> "date": "2020-04-01T00:00:00+00:00"
> },
> {
> "amount": 32549688,
> "date": "2020-05-01T00:00:00+00:00"
> },
> {
> "amount": 39679000,
> "date": "2020-06-01T00:00:00+00:00"
> },
> {
> "amount": 49795412,
> "date": "2020-07-01T00:00:00+00:00"
> },
> {
> "amount": 36606076,
> "date": "2020-08-01T00:00:00+00:00"
> },
> {
> "amount": 57962424,
> "date": "2020-09-01T00:00:00+00:00"
> },
> {
> "amount": 36177500,
> "date": "2020-10-01T00:00:00+00:00"
> },
> {
> "amount": 52268468,
> "date": "2020-11-01T00:00:00+00:00"
> },
> {
> "amount": 48258608,
> "date": "2020-12-01T00:00:00+00:00"
> },
> {
> "amount": 57309564,
> "date": "2021-01-01T00:00:00+00:00"
> },
> {
> "amount": 58404696,
> "date": "2021-02-01T00:00:00+00:00"
> },
> {
> "amount": 79738670,
> "date": "2021-03-01T00:00:00+00:00"
> },
> {
> "amount": 64431656,
> "date": "2021-04-01T00:00:00+00:00"
> },
> {
> "amount": 80836710,
> "date": "2021-05-01T00:00:00+00:00"
> },
> {
> "amount": 278838140,
> "date": "2021-06-01T00:00:00+00:00"
> },
> {
> "amount": 152083840,
> "date": "2021-07-01T00:00:00+00:00"
> },
> {
> "amount": 58704888,
> "date": "2021-08-01T00:00:00+00:00"
> },
> {
> "amount": 88224360,
> "date": "2021-09-01T00:00:00+00:00"
> },
> {
> "amount": 170566320,
> "date": "2021-10-01T00:00:00+00:00"
> },
> {
> "amount": 141366370,
> "date": "2021-11-01T00:00:00+00:00"
> },
> {
> "amount": 105627490,
> "date": "2021-12-01T00:00:00+00:00"
> },
> {
> "amount": 94905890,
> "date": "2022-01-01T00:00:00+00:00"
> },
> {
> "amount": 95359040,
> "date": "2022-02-01T00:00:00+00:00"
> },
> {
> "amount": 2543117600,
> "date": "2022-03-01T00:00:00+00:00"
> },
> {
> "amount": 211435150,
> "date": "2022-04-01T00:00:00+00:00"
> },
> {
> "amount": 143630210,
> "date": "2022-05-01T00:00:00+00:00"
> },
> {
> "amount": 143939470,
> "date": "2022-06-01T00:00:00+00:00"
> },
> {
> "amount": 196693730,
> "date": "2022-07-01T00:00:00+00:00"
> },
> {
> "amount": 100334656,
> "date": "2022-08-01T00:00:00+00:00"
> },
> {
> "amount": 183941920,
> "date": "2022-09-01T00:00:00+00:00"
> },
> {
> "amount": 173492930,
> "date": "2022-10-01T00:00:00+00:00"
> },
> {
> "amount": 182397790,
> "date": "2022-11-01T00:00:00+00:00"
> },
> {
> "amount": 130861856,
> "date": "2022-12-01T00:00:00+00:00"
> },
> {
> "amount": 759725,
> "date": "2023-01-01T00:00:00+00:00"
> },
> {
> "amount": 2829197,
> "date": "2023-02-01T00:00:00+00:00"
> },
> {
> "amount": 18720862,
> "date": "2023-03-01T00:00:00+00:00"
> },
> {
> "amount": 1111510,
> "date": "2023-04-01T00:00:00+00:00"
> },
> {
> "amount": 2608219,
> "date": "2023-05-01T00:00:00+00:00"
> },
> {
> "amount": 919761,
> "date": "2023-06-01T00:00:00+00:00"
> },
> {
> "amount": null,
> "date": "2023-07-01T00:00:00+00:00"
> },
> {
> "amount": null,
> "date": "2023-08-01T00:00:00+00:00"
> },
> {
> "amount": null,
> "date": "2023-09-01T00:00:00+00:00"
> },
> {
> "amount": null,
> "date": "2023-10-01T00:00:00+00:00"
> },
> {
> "amount": null,
> "date": "2023-11-01T00:00:00+00:00"
> },
> {
> "amount": null,
> "date": "2023-12-01T00:00:00+00:00"
> },
> {
> "amount": null,
> "date": "2024-01-01T00:00:00+00:00"
> }
> ]
> }
> }
> 
> ```

Here is a small proof of concept in Julia [DataJune.jl/transaction\_timeseries.jl at drafts - DataJune.jl - Service Git](https://git.42l.fr/HugoTrentesaux/DataJune.jl/src/branch/drafts/scripts/transaction_timeseries.jl) allowing to produce the following plot:

 ![image](https://forum.duniter.org/uploads/default/original/2X/7/7707388948d9c0515af199768e33361647343cc8.png)

As you can see, the transactions trace the history of Ğ1 until the ĞDev migration. This tool could be adapted to Ğ1 and used before the v2 migration.